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Generalized method of moment estimation of multivariate multifractal models

journal contribution
posted on 01.12.2017, 00:00 authored by Ruipeng LiuRuipeng Liu, T Lux
Multifractal processes have recently been introduced as a new tool for modeling the stylized facts of financial markets and have been found to consistently provide certain gains in performance over basic volatility models for a broad range of assets and for various risk management purposes. Due to computational constraints, multivariate extensions of the baseline univariate multifractal framework are, however, still very sparse so far. In this paper, we introduce a parsimoniously designed multivariate multifractal model, and we implement its estimation via a Generalized Methods of Moments (GMM) algorithm. Monte Carlo studies show that the performance of this GMM estimator for bivariate and trivariate models is similar to GMM estimation for univariate multifractal models. An empirical application shows that the multivariate multifractal model improves upon the volatility forecasts of multivariate GARCH over medium to long forecast horizons.

History

Journal

Economic Modelling

Volume

67

Pagination

136 - 148

Publisher

Elsevier

Location

Amsterdam, The Netherlands

ISSN

0264-9993

Language

eng

Publication classification

C1 Refereed article in a scholarly journal; C Journal article

Copyright notice

2016 Elsevier B.V.